As parallel packages for computational science become more sophisticated, it becomes more difficult for a researcher to understand the most important factors that determine end-to-end productivity from initial input data to final result. Aspects such as file IO and data transfer can be just as important in practice as the performance and parallel scalability of the application itself. This course will take a holistic approach and cover tools and techniques to help researchers to improve their overall scientific productivity on large-scale HPC systems.
The course aims to answer the following questions:
How can I understand the end-to-end performance of my research workflow?
How do I measure parallel application performance and which metrics should I use and when?
What practical steps can I take to improve my research workflow?
Participants must have attended ARCHER2 for software package users or be familiar with use of Software Packages on ARCHER and/or ARCHER2
This course focuses on efficient use of centrally installed packages. Users interested in optimising the performance of their own applications, e.g. through compiler options or code changes, should consider attending Performance Optimisation on AMD EPYC instead
Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on.
They are also required to abide by the ARCHER2 Training Code of Conduct.
Details to follow